Problems with Conventional Analog Integration in Machine Monitoring Systems
Conversion from one type of vibration-related signal (such as acceleration) to another vibration-related signal (such as velocity or displacement) is a common requirement for vibration monitoring systems. A typical example is the conversion from acceleration to velocity by integration of the acceleration signal. Similarly, the opposite conversion can be performed by differentiating a velocity signal. In the past, these conversions have been done using analog hardware filters. Such conversions have also been done after data collection, using software that performs a Fast Fourier Transform (FFT) and operates on the transformed data in the frequency domain.
An ideal hardware integrator is shown in FIG. 4. This circuit directly converts an acceleration (or velocity) signal to velocity (or displacement) with a conversion factor proportional to 1/R1×C2. Unfortunately, this circuit is not suitable in practice due to the high DC gain. The circuit quickly saturates due to offset currents and voltages of the operational amplifier. A more refined approach is shown in FIG. 5, where the addition of R2 and C1 limit the low-frequency response of the operational amplifier to prevent saturation. The appropriate selection of R1 and C2 gives a direct conversion between units, (e.g. 61.45/f for conversion of acceleration to velocity). This approach converts the signal directly, prior to data acquisition, so that no additional data processing is required. However, it offers no flexibility in changing the conversion factors and is subject to variability in hardware component values. Also, it consumes large amounts of circuit board real estate due to the physically large components required for low-frequency operation.
Another prior art approach to the conversion is to digitize the vibration signal using an analog-to-digital converter (ADC), transform to the frequency domain using FFT methods, and apply integration or differentiation on the frequency spectrum. This process is depicted in FIG. 6. Disadvantages of this approach include the lack of ability to do the conversion process continuously in real-time and the system complexity required to perform the FFT. Also, creation of an integrated time waveform requires extensive data processing (i.e., forward and inverse FFT computations). Finally, the FFT method assumes the signal is stationary which may not be true for dynamic signal conditions and could lead to errors in the re-creation of the time domain signal.
What is needed, therefore, is a conversion process that reduces hardware complexity, reduces data storage requirements, and provides for direct integration or differentiation of time-domain vibration waveforms without resorting to FFT methods.
Problems with Conventional Analog Signal Conditioning in Machine Monitoring Systems
As shown in FIG. 7, a typical vibration analysis channel 50 consists of an analog front-end 52, an analog-to-digital converter (ADC) 54, and a digital signal processor (DSP) 56 or microcontroller. The analog front-end 52 usually contains a vibration sensor 58, an input amplifier 60, an AC coupling amplifier 62, analog integrator 64, a variable-gain amplifier 66, and a low-pass anti-aliasing filter and high-pass filter 68.
Such implementations of front-end signal conditioning functions in the analog domain cause numerous problems. Calibration is required due to component variations which cause the sensitivity and bandwidth of the signal path to vary. Analog components require relatively large amounts of space on the printed circuit board, and they consume large amounts of power for low-noise designs. They are also somewhat limited in terms of programmability. For systems designed for use in hazardous environments, reduced voltage and capacitor allowances force tradeoffs in noise and bandwidth in the analog signal path.
What is needed, therefore, is a machine vibration measurement system in which the front-end signal conditioning functions are performed in the digital domain, such as in a field programmable gate array (FPGA) or application-specific integrated circuit (ASIC), or as an algorithm in a digital signal processor.
A discussion of prior art machinery vibration analyzers will provide further context for understanding the various advantages of the machine vibration measurement system of the present invention. U.S. Pat. No. 5,412,985 to Garcia et. al. (hereinafter “Garcia), U.S. Pat. No. 5,633,811 to Canada et. al. (hereinafter “Canada”), U.S. Pat. No. 5,965,819 to Piety et. al. (hereinafter “Piety”), and US 2006015738A1 to Leigh (hereinafter “Leigh”) are representative of such prior art machinery vibration analyzers.
Garcia et. al. discloses using either an IIR or FIR filter in a machinery vibration analyzer. It incorporates analog signal conditioning, including integration, and it requires anti-aliasing filtering before analog-to-digital conversion.
Canada describes a machinery vibration analyzer having analog signal conditioning, including analog integration, direct-current (DC) offset, gain control, and a fixed frequency low-pass anti-aliasing filter. In addition to this analog circuitry, the disclosure teaches about digital filtering, decimation, and sigma-delta noise shaping.
Piety describes parallel processing in a vibration analyzer wherein an analog sensor signal representing a measured property of an operating machine is split and simultaneously processed through at least two parallel circuits. Each of these circuits has input filters, integrators, DC offsets, amplifiers, and circuit filters prior to parallel analog-to-digital conversion. Each parallel circuit is capable of performing different types of signal analyses with varying analog signal conditioning and sampling rate requirements.
Leigh describes machinery vibration analysis that involves deriving multiple types of vibration signals from one vibration signal and selecting a digital acceleration signal or first digital integration to convert a digital acceleration signal to a velocity signal or a first digital integration to convert a digital acceleration signal to a velocity signal followed by a second digital integration to convert a velocity signal to a displacement signal in a machinery vibration analyzer. The vibration analyzer according to Leigh incorporates analog signal conditioning acting on the analog signal from an accelerometer, including scaling, DC offset, and anti-alias filtering. Leigh requires selection of a sampling frequency before digitizing the analog acceleration signal using an analog-to-digital converter (ADC).
The machinery vibration analyzers disclosed by Garcia, Canada, Piety, and Leigh do not teach about the following elements found in certain embodiments of the present invention:                (a) fixed analog-to-digital sampling rate in an analog-to-digital converter;        (b) flexible field programmability for a parallel vibration signal processing circuit;        (c) parallel vibration signal processing in an FPGA;        (d) a parallel vibration signal processing in an ASIC;        (e) an ideal integrator transfer function using a difference equation;        (f) a synthesized sampling rate using an arbitrary resampler;        (g) a digital filter to remove a direct current (DC) component from a vibration signal;        (h) switch control circuitry for switching between a non-rechargeable battery and an energy harvester power source;        (i) a digital implementation of an anti-aliasing filter;        (j) a digital implementation of a scaling circuit;        (k) replacement of a traditional analog signal conditioning component calibration with a digital design that does not require analog calibration;        (l) a single-step double integrator in the digital domain that converts an acceleration signal to a displacement signal; and        (m) a field programmable switching device that is operable to direct any one of a plurality of digital vibration inputs to any one of a plurality of outputs.        
U.S. Pat. No. 5,696,420 to Inanaga et. al. (hereinafter Inanaga) and U.S. Pat. No. 7,164,853 to Tomita (hereinafter Tomita) describe controlling devices for detecting a motion of the device itself.
Inanaga describes a control device for detecting a swinging motion of a person's head wearing audio headphones. The Inanaga device uses a vibration type gyroscope that reads a control signal and controls an audio signal to create virtual sound source positioning in reference to a direction of the listener wearing headphones. Inanaga teaches using a digital integrator and digital differentiator with a digital filter, such as an infinite impulse response (IIR) digital filter, finite impulse response (FIR) digital filter, or the like. The vibration type gyroscope of Inanaga is a control device and not a measurement apparatus like the present invention. For example the following features that are required for the Inanaga apparatus are not required for the present invention (that is, these features may be avoided individually or collectively with the present invention):                (a) an amplitude-modulated detection signal is converted into a digital signal;        (b) a modulated analog piezoelectric signal output is demodulated to obtain a correct detection output;        (c) piezoelectric elements are integrated into the apparatus body;        (d) a pair of piezoelectric elements are used for detection, and a pair of piezoelectric elements are used for driving, and a differential amplifier is used for obtaining a differential output between output signals of the pair of piezoelectric elements for detection; and        (e) a control signal is supplied from the outside.        
Tomita describes a vibration correcting optical control device. This device detects vibration caused by hand movement or the like to provide control for correction of optical blur. This disclosure mentions using a low pass filter such as an FIR filter or an IIR filter. The disclosure also teaches a digital integrating operation unit. The disclosure according to Tomita requires multiple things that are not required in the present invention (that is, these features may be avoided individually or collectively):                (a) an angular speed sensor capable of detecting coriolis force;        (b) a vibration detection and signal processing unit;        (c) a reference value calculation unit;        (d) determination of an abnormal vibration indication is required before performing an integration or a differentiation; and        (e) a drive signal calculation unit.        
Furthermore, a chasm of undisclosed applications exists between the Inanaga and Tomita control devices and the machinery vibration analyzer of the present invention. Even if the disclosures of Inanaga and Tomita are combined, the combination fails to describe or suggest several important features of various embodiments of the present invention, such as:                (a) measuring a vibration signal that is indicative of the vibration level of a machine;        (b) measuring a parameter of a machine that is indicative of a machine fault condition or machine performance;        (c) sensing an acceleration parameter of a machine;        (d) field programmability;        (e) FPGA processing;        (f) ASIC processing;        (g) selections;        (h) removing a DC signal component;        (i) sampling an analog vibration signal at a fixed sampling rate; and        (j) synthesizing other sampling rates from a fixed sampling rate.Besides those listed here, there are many other examples of features desired for machinery vibration analysis that are not provided by Inanaga or Tomita.        